使用机器学习创建和检测物联网僵尸网络

H. Esha, Basanagouda S Hadimani, S. P. Devika, P. Shanthala, R. Bhavana
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引用次数: 0

摘要

根据道德黑客专家的说法,恶意软件已经在网络环境中存在了很长时间。随着新技术的发展,越来越多的恶意软件在网上传播。僵尸网络的流行已经增加。受感染的软件首先创建一个僵尸网络,然后在整个网络中传播僵尸网络。僵尸网络通常用于大量计算机被感染的场景。鉴于僵尸网络感染大量计算机的能力,道德黑客指导员发出了警告。网络安全越来越依赖于僵尸网络。僵尸网络攻击绝大多数企业。为了解决这个问题,研究人员开始使用机器学习(ML)。在本研究中,我们快速回顾了用于僵尸网络识别的各种机器学习(ML)技术。本文的主要目标是进行各种恶意软件分析来构建我们的僵尸网络,使用包捕获工具进行分析,并利用僵尸网络进一步验证我们的机器学习模型。为了开发可靠和有效的实时在线检测和缓解系统以及更可靠的模型,全面了解这些功能至关重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
IoT Botnet Creation and Detection using Machine Learning
According to ethical hacking specialists, malware has been present in online environments for a very long time. More malware is being disseminated online as new technology is developed. The prevalence of botnets has increased. Infected software first creates a botnet before spreading the bot throughout a network. A botnet is employed in scenarios with infected massive computers. Ethical hacking instructors issue a warning due to the botnet’s ability to infect vast numbers of computers. Cybersecurity is increasingly relying on botnets. Botnets attack the vast majority of enterprises. To tackle this issue, researchers began to use machine learning (ML).We give a quick review of the various machine learning (ML) techniques used in botnet identification in this study. This paper’s main goal is to undertake various malware analysis to build our botnet, analyze it using a packet-capturing tool, and further validate our machine-learning models utilizing the botnet. To develop reliable and effective real-time online detection and mitigation systems as well as more reliable models, a comprehensive knowledge of these functions is essential.
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